DETAILED ACTION
This Non-Final Office Action is in response to the originally filed specification [September 9, 2025] and claim amendments [November 26, 2025].
Claims 18, 22, 23, and 28-62 have been cancelled.
Claims 1-17, 19-21, and 24-27 are currently pending and have been considered below.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-17, 19-21, and 24-27 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards non-eligible subject matter.
In terms of Step 1, claims 1-17, 19-21, and 24-27 are directed towards one of the four categories of statutory subject matter.
In terms of Step 2(a)(1), independent claims 1, 26, and 27 are directed towards (as represented by claim 1), “A method for matching of job candidates to job openings, the method comprising: obtaining a job request, the job request including data related to an open job position; determining one or more recommended candidates for the job request, at least in part by: identifying a first plurality of candidate profiles based on the job request, each candidate profile being associated with a respective candidate; providing, as inputs to a first model, the job request and a first set of questions related to the job request; generating, using the first model, a first set of answers based on the job request and the first set of questions; providing, as inputs to the first model, the first plurality of candidate profiles and a second set of questions related to the candidate profiles; generating, using the first model, a plurality of second sets of answers based on the plurality of candidate profiles and the second set of questions; and determining the one or more recommended candidates based on the first and second sets of answers; and displaying a representation of the one or more recommended candidates”. The claims are describing a collection of information, a high level analysis utilizing a first model and question/answer inputs, and a display of the results. The claims are directed towards a mental process that a person could perform with pen and paper, specifically towards the aspects of job candidates and profiles that are analyzed with a model that could be attributed to a person asking inter-dialogue questions and answers about a candidate pool to narrow the selection process and then providing a final list of candidates based on the questioning. As such, the claims are directed towards an abstract idea under the mental process grouping.
Additionally, the claims are describing a business relation and commercial activity in terms of providing steps that a hiring manager performs on a candidate pool by selecting or filtering down the number of available candidates based on different analysis and questions to determine a final selection or group of candidates at different parts of the hiring process (interview selection, employment offer selection, onboarding, etc). Therefore, the claims are further directed towards an abstract idea under the certain method of organizing human activity grouping.
Step 2(a)(II) considers the additional elements in terms of being transformative into a practical application. The additional elements of claims 1, 26, and 27 are, “A system for matching of job candidates to job openings, the system comprising: a computer hardware processor; a non-transitory computer readable storage medium, storing processor-executable instructions, that when executed by the computer hardware processor, cause the processor to perform a method for matching job candidates to a job opening (claim 26); At least one non-transitory computer readable storage medium, storing processor-executable instructions, that when executed by a computer hardware processor, cause the processor to perform a method for matching job candidates to a job opening, the method comprising (claim 27); from a database; trained machine learning model; on a user interface”. The additional elements are described in the originally filed specification [65-66, 119-120, and 150-159]. The additional elements are merely described in terms of tools to implement the abstract idea. The computer elements are generic computer components and the machine learning model is described as a generic named technique “LLM” to implement the analysis for the identified abstract idea(s). The additional elements are not directed towards technical improvement, but rather generic technology that are mere instructions to apply. Therefore, the claims are not directed towards additional elements that are transformative into a practical application. Refer to MPEP 2106.05(f).
Step 2(b) considers the additional elements in terms of being significantly more than the identified abstract idea. The additional elements of claims 1, 26, and 27 are, “A system for matching of job candidates to job openings, the system comprising: a computer hardware processor; a non-transitory computer readable storage medium, storing processor-executable instructions, that when executed by the computer hardware processor, cause the processor to perform a method for matching job candidates to a job opening (claim 26); At least one non-transitory computer readable storage medium, storing processor-executable instructions, that when executed by a computer hardware processor, cause the processor to perform a method for matching job candidates to a job opening, the method comprising (claim 27); from a database; trained machine learning model; on a user interface”. The additional elements are described in the originally filed specification [65-66, 119-120, and 150-159]. The additional elements are merely described in terms of tools to implement the abstract idea. The computer elements are generic computer components and the machine learning model is described as a generic named technique “LLM” to implement the analysis for the identified abstract idea(s). The additional elements are not directed towards technical improvement, but rather generic technology that are mere instructions to apply. Therefore, the claims are not directed towards additional elements that are significantly more than the identified abstract idea. Refer to MPEP 2106.05(f).
Dependent claims 3-13 are further describing the abstract idea and are not directed towards additional elements beyond those identified above. The claims are directed towards, “wherein the data of the job request includes one or more of: an education requirement for the open position, a location for the open position, a salary for the open position, skills for the open position, certifications for the open position, daily responsibilities for the open position, workplace preferences for the open position, and order intake summaries for the open position”, “wherein a first candidate profile of the plurality of candidate profiles comprises data including a resume for a first candidate associated with the first candidate profile and one or more of: demographic information associated with the first candidate, recruiter notes associated with the first candidate, and interview notes associate with the first candidate”, “wherein identifying the first plurality of candidate profiles comprises an act of comparing data of a second plurality of candidate profiles stored in the database to data of the job request and identifying the first plurality of candidate profiles from the second plurality of candidate profiles based on the act of comparing”, “wherein the comparing comprises determining a level of matching between the data of the second plurality of candidate profiles and the data of the job request, and wherein candidate profiles of the first plurality of candidate profiles are identified when the level of matching exceeds a threshold level”, “wherein the first set of questions include questions related to desired skills for the open position, required skills for the open position, workplace attributes for the open position, and responsibilities for the open position”, “wherein the first set of questions is structured as a list of questions and further includes instructions for the first trained machine learning model to use in generating the first set of answers”, “wherein the second set of questions include questions related to skills of a candidate, work history of the candidate, education of a candidate, past workplace attributes of the candidate, and past performance of the candidate”, “wherein the second set of questions is structured as a list of questions and further includes instructions for the first trained machine learning model to use in generating the plurality of second sets of answers”, “wherein the second set of questions further includes instructions for determining the plurality of second sets of answers based on content of the candidate profiles of the first plurality of candidate profiles”, “wherein the first set of answers is structured as a list of answers to each of the first set of questions and the second set of answers is structured as a list of answers to each of the second set of questions”, and “wherein the first set of answers is structured as a paragraph summarizing answers to the first set of questions and the second set of answers are structured as a paragraph summarizing answers to the second set of questions”. The claims are further describing the mental process through different elements of the collect, analyze, and display components. The collection is based on the type of information and question inputs that are utilized in the analysis, the high level analysis is further defined by a threshold, instructions to determine a set of answers, and comparing the profiles, and the results are further described in terms of the outputs with respect to structure of the answers (generating a structured list) and providing summarization answers. The claims are further describing the mental process with respect to the collect, high level analysis, and display the results. Further, in terms of the certain method of organizing human activity, the claims are describing the hiring analysis and process at different stages and aspects. The claims provide hiring analysis with respect to a candidate’s qualifications and other job requirements, providing a summarization of the answers to the posited questions, generating questions regarding candidate skills and other work performance criteria, and selecting profiles accordingly. The claims are further describing the abstract idea under the certain method of organizing human activity grouping. The additional elements of the computer aspects and the machine learning were considered above with respect to the independent claims and no further additional elements are provided. As such, the claims are directed towards the abstract idea and are not directed towards additional elements that are transformative into a practical application or significantly more than the identified abstract idea. Refer to MPEP 2106.05(f).
Dependent claims 2, 14-17, 19, and 25 are further directed towards additional elements beyond those identified above. The claims are directed towards, “wherein determining the one or more recommended candidates based on the first and second sets of answers comprises: generating, using a second trained machine learning model, a first data embedding based on the first set of answers and a plurality of second data embeddings based on the plurality of second sets of answers; and determining, using a third trained machine learning model, a plurality of candidate prediction scores, each one of the plurality of candidate prediction scores being determined for each of plurality of second data embeddings, based on comparisons of the first data embedding and each of the plurality of second data embeddings, wherein an act of determining the one or more recommended candidates is performed responsive to the act of determining the candidate prediction scores”, “wherein the first trained machine learning model is a large language model with a decoder-only transformer architecture”, “wherein the first trained machine learning model is fine- tuned for analysis of candidate profiles and job requests”, “wherein the second trained machine learning model is a large language model with an encoder-only transformer architecture and is fine-tuned to analyze information related to job postings and candidate profiles”, “wherein the third trained machine learning model is an artificial neural network configured to determine the candidate prediction scores based on the first data embedding and the second data embeddings”, “wherein the third trained machine learning model is trained using simulated candidate profiles, the simulated candidate profiles being generated using historic candidate profiles associated with one or more historic job requests stored in the database”, and “further comprising: obtaining updated candidate profiles of one or more candidates of the recommended candidates, the updated candidate profiles comprising data related to the associated candidates with respect to the open job position; providing, as inputs to the first trained machine learning model the updated candidate profiles and a third set of questions related to the data related to the candidate profiles with respect to the open job position; generating, using the first trained machine learning model, a plurality of third sets of answers based on the updated candidate profiles and the third set of questions; generating, using the second trained machine learning model, a plurality of third data embeddings based on the plurality of third sets of answers; and determining, using the third trained machine learning model, updated candidate prediction scores for the candidates associated with each of plurality of third data embeddings”. The additional elements are with respect to the second and third machine learning model, the named modeling techniques (encoder-only, decoder-only, neural network, fine-tuned analyzed, neural network), and a simulated candidate. The additional elements are described in the originally filed specification [67-69, 92-97, and 129-132]. The additional elements are merely describing generic technology to implement the abstract idea. The simulated candidate is providing an idea of a solution but there is no specific technical description beyond providing high level analysis and discussion regarding the solution. Further, the second and third machine learning model and named techniques are generic technology as a tool to implement the abstract idea. The specification is not describing an improvement to the techniques themselves, but rather utilizing generic, named ML models and training techniques to implement the abstract idea. Therefore, the claims are directed towards an abstract idea and the additional elements are not transformative into a practical application or significantly more. Refer to MPEP 2106.05(f).
Dependent claims 20, 21, and 24 are further describing the abstract idea and are not directed towards additional elements beyond those identified above. The claims are directed towards, “further comprising: obtaining updated candidate profiles of one or more candidates of the recommended candidates, the updated candidate profiles comprising new data related to the associated candidates with respect to the open job position”, “wherein the new data comprises at least one of: notes on the candidates provided by one or more recruiter users; notes on one or more candidate interviews conducted in relation to the open position; or transcripts of one or more candidate interviews conducted in relation to the open job position” and, “further comprising: providing, as inputs to the first trained machine learning model the updated candidate profiles and a third set of questions related to the new data, with respect to the open job position; generating, using the first trained machine learning model, a plurality of third sets of answers based on the updated candidate profiles and the third set of questions; and updating the recommended candidates based on the plurality of third sets of answers”. The claims are further describing, with respect to the mental process, aspects of the collection, high level analysis, and display. The collection is based on the updated candidate profiles and further input elements for the machine learning with respect to the job position, recruiter notes, and candidate interviews. The analysis is further described in terms of the utilization of the first model (with the above-considered trained machine learning model), and the display is providing/updating the recommended candidates based on the new pool. These elements further describe the hiring process for the business relation in terms of a hiring manager receiving an updated or new batch of recruits that are then analyzed with respect to the current selection to update the candidate pool for the job opportunity. The additional elements of the machine learning were considered above with respect to the independent claims and no further additional elements are provided. As such, the claims are directed towards the abstract idea and are not directed towards additional elements that are transformative into a practical application or significantly more than the identified abstract idea. Refer to MPEP 2106.05(f).
The claimed invention is describing an abstract without additional elements that are significantly more or transformative into a practical application. Therefore, claims 1-17, 19-21, and 24-27 are rejected under 35 USC 101 for being directed towards non-eligible subject matter.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-13, 17, 19-21, 24-25, and 26-27 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wong et al [2023/0088444], hereafter Wong.
Regarding claim 1, Wong discloses a method for matching of job candidates to job openings, the method comprising: obtaining a job request, the job request including data related to an open job position; determining one or more recommended candidates for the job request (Fig 1 and paragraphs [29-33]; Wong discloses providing a job description and a matching system identifying candidates in a talent pool.),
at least in part by: identifying a first plurality of candidate profiles from a database based on the job request, each candidate profile being associated with a respective candidate; providing, as inputs to a first trained machine learning model, the job request and a first set of questions related to the job request; generating, using the first trained machine learning model, a first set of answers based on the job request and the first set of questions; providing, as inputs to the first trained machine learning model, the first plurality of candidate profiles and a second set of questions related to the candidate profiles; generating, using the first trained machine learning model, a plurality of second sets of answers based on the plurality of candidate profiles and the second set of questions (Paragraphs [33-40] Wong discloses that the system analyzes using neural network and other ML models to provide interview/assessment questions based on the candidate interview and assessments. Wong further provides the assessments based on chatbots and other collected information that fall within the interpretation of the set of questions and answers.); and
determining the one or more recommended candidates based on the first and second sets of answers; and displaying a representation of the one or more recommended candidates on a user interface (Paragraph [42]; Wong discloses that a list of recommended candidates is selected and presented based on the analysis of the interview and other candidate evaluation elements.).
Regarding claim 2, Wong further discloses the method of claim 1, wherein determining the one or more recommended candidates based on the first and second sets of answers comprises: generating, using a second trained machine learning model, a first data embedding based on the first set of answers and a plurality of second data embeddings based on the plurality of second sets of answers; and determining, using a third trained machine learning model, a plurality of candidate prediction scores, each one of the plurality of candidate prediction scores being determined for each of plurality of second data embeddings, based on comparisons of the first data embedding and each of the plurality of second data embeddings, wherein an act of determining the one or more recommended candidates is performed responsive to the act of determining the candidate prediction scores (Figs 1, 16, 17, 18A, 18B, and paragraphs [33-36 and 44-49]; Wong discloses the ML models and utilization of nodes (further discussed in [59-65] for the BOK and neural network models. These elements compare the candidate profile data including the evaluation) with the job description data to provide the recommended candidate based on the analysis.).
Regarding claim 3, Wong further discloses the method of claim 1, wherein the data of the job request includes one or more of: an education requirement for the open position, a location for the open position, a salary for the open position, skills for the open position, certifications for the open position, daily responsibilities for the open position, workplace preferences for the open position, and order intake summaries for the open position (Paragraphs [50-52]; Wong discloses the attributes for the job request including education level, soft skills, and other criteria.).
Regarding claim , Wong further discloses the method of claim 1, wherein a first candidate profile of the plurality of candidate profiles comprises data including a resume for a first candidate associated with the first candidate profile and one or more of: demographic information associated with the first candidate, recruiter notes associated with the first candidate, and interview notes associate with the first candidate (Paragraph [33]; Candidate information includes the resume and interview results (emotional and other activity).).
Regarding claim 5, Wong further discloses the method of claim 1, wherein identifying the first plurality of candidate profiles comprises an act of comparing data of a second plurality of candidate profiles stored in the database to data of the job request and identifying the first plurality of candidate profiles from the second plurality of candidate profiles based on the act of comparing (paragraph [42 and 52-55]; Wong discloses that the candidate profiles are compared based on peers and other profile data.).
Regarding claim 6, Wong further discloses the method of claim 5, wherein the comparing comprises determining a level of matching between the data of the second plurality of candidate profiles and the data of the job request, and wherein candidate profiles of the first plurality of candidate profiles are identified when the level of matching exceeds a threshold level (Paragraphs [38 and 42-43]; Wong discloses the utilization of thresholds for candidate identification and evaluation.).
Regarding claim 7, Wong further discloses the method of claim 1, wherein the first set of questions include questions related to desired skills for the open position, required skills for the open position, workplace attributes for the open position, and responsibilities for the open position (Paragraphs [33-34 and 42-48]; Wong discloses providing questions for a candidate evaluation based on skills, attributes, and other elements. Examiner notes that what the questions are merely describes non-functional descriptive material that provides printed matter for a human reader and does not provide a functional relationship within the claim. Refer to MPEP 2111.05.).
Regarding claim 8, Wong further discloses the method of claim 7, wherein the first set of questions is structured as a list of questions and further includes instructions for the first trained machine learning model to use in generating the first set of answers (Paragraphs [33-34 and 42-48]; Wong discloses providing questions for a candidate evaluation based on skills, attributes, and other elements. The BOK includes a list of questions that the ML/RNN model utilizes in generating the candidate evaluation.).
Regarding claim 9, Wong further discloses the method of claim 1, wherein the second set of questions include questions related to skills of a candidate, work history of the candidate, education of a candidate, past workplace attributes of the candidate, and past performance of the candidate (Paragraphs [33-34 and 42-48]; Wong discloses providing questions for a candidate evaluation based on skills, attributes, and other elements. Examiner notes that what the questions are merely describes non-functional descriptive material that provides printed matter for a human reader and does not provide a functional relationship within the claim. Refer to MPEP 2111.05.).
Regarding claim 10, Wong further discloses the method of claim 9, wherein the second set of questions is structured as a list of questions and further includes instructions for the first trained machine learning model to use in generating the plurality of second sets of answers (Paragraphs [33-34 and 42-48]; Wong discloses providing questions for a candidate evaluation based on skills, attributes, and other elements. The BOK includes a list of questions that the ML/RNN model utilizes in generating the candidate evaluation.).
Regarding claim 11, Wong further discloses the method of claim 10, wherein the second set of questions further includes instructions for determining the plurality of second sets of answers based on content of the candidate profiles of the first plurality of candidate profiles (Paragraphs [44-45]; Wong discloses that the list of questions is provided and updated based on candidate evaluations and answers.).
Regarding claim 12, Wong further discloses the method of claim 1, wherein the first set of answers is structured as a list of answers to each of the first set of questions and the second set of answers is structured as a list of answers to each of the second set of questions (Paragraphs [34-35 and 41-43]; Wong discloses the structured answers based on the questions. This is interpreted with respect to the skill evaluation and dynamic question to select the follow-up questions.).
Regarding claim 13, Wong further discloses the method of claim 1, wherein the first set of answers is structured as a paragraph summarizing answers to the first set of questions and the second set of answers are structured as a paragraph summarizing answers to the second set of questions (Paragraphs [48]; Wong discloses summarization answers for the analysis and reporting elements.).
Regarding claim 17, Wong further discloses the method of claim 2, wherein the third trained machine learning model is an artificial neural network configured to determine the candidate prediction scores based on the first data embedding and the second data embeddings (Paragraphs [31-36]; Wong discloses neural network models for candidate evaluation.).
Regarding claim 19, Wong further discloses the method of claim 2, wherein the third trained machine learning model is trained using simulated candidate profiles, the simulated candidate profiles being generated using historic candidate profiles associated with one or more historic job requests stored in the database (paragraph [48]; Wong discloses that the system trains the ML based on historical profiles and job requests evaluations.).
Regarding claim 20, Wong further discloses the method of claim 1, further comprising: obtaining updated candidate profiles of one or more candidates of the recommended candidates, the updated candidate profiles comprising new data related to the associated candidates with respect to the open job position (Paragraphs [37-39 and 48-53]; Wong discloses updating the profile and candidate evaluation based on candidate interview and other evaluations provided by the ML/AI analysis.).
Regarding claim 21, Wong further discloses the method of claim 20, wherein the new data comprises at least one of: notes on the candidates provided by one or more recruiter users; notes on one or more candidate interviews conducted in relation to the open position; or transcripts of one or more candidate interviews conducted in relation to the open job position (Paragraphs [35-39 and 48-53]; Wong discloses updating the profile and candidate evaluation based on candidate interview and other evaluations provided by the ML/AI analysis.).
Regarding claim 24, Wong further discloses the method of claim 20, further comprising: providing, as inputs to the first trained machine learning model the updated candidate profiles and a third set of questions related to the new data, with respect to the open job position; generating, using the first trained machine learning model, a plurality of third sets of answers based on the updated candidate profiles and the third set of questions; and updating the recommended candidates based on the plurality of third sets of answers (Paragraphs [35-40 and 48-53]; Wong discloses updating the profile and candidate evaluation based on candidate interview and other evaluations provided by the ML/AI analysis. Wong further discloses the adaptive interview question that is based on updating the question type within the bank based on candidate profile and previous answered questions.).
Regarding claim 25, Wong further discloses the method of claim 2, further comprising: obtaining updated candidate profiles of one or more candidates of the recommended candidates, the updated candidate profiles comprising data related to the associated candidates with respect to the open job position; providing, as inputs to the first trained machine learning model the updated candidate profiles and a third set of questions related to the data related to the candidate profiles with respect to the open job position; generating, using the first trained machine learning model, a plurality of third sets of answers based on the updated candidate profiles and the third set of questions; generating, using the second trained machine learning model, a plurality of third data embeddings based on the plurality of third sets of answers; and determining, using the third trained machine learning model, updated candidate prediction scores for the candidates associated with each of plurality of third data embeddings (Paragraphs [35-40 and 48-53]; Wong discloses updating the profile and candidate evaluation based on candidate interview and other evaluations provided by the ML/AI analysis. Wong further discloses the adaptive interview question that is based on updating the question type within the bank based on candidate profile and previous answered questions. Wong further discloses the ML models and utilization of nodes (further discussed in [59-65] for the BOK and neural network models. These elements compare the candidate profile data including the evaluation) with the job description data to provide the recommended candidate based on the analysis.).
Regarding claim 26, Wong discloses a system for matching of job candidates to job openings, the system comprising: a computer hardware processor; a non-transitory computer readable storage medium, storing processor-executable instructions, that when executed by the computer hardware processor, cause the processor to perform a method for matching job candidates to a job opening (Fig 6 and paragraphs [44]; Wong discloses the system elements to implement the job candidate evaluation.),
the method comprising: obtaining a job request, the job request including data related to an open job position; and determining one or more recommended candidates for the job request (Fig 1 and paragraphs [29-33]; Wong discloses providing a job description and a matching system identifying candidates in a talent pool.),
at least in part by: identifying a first plurality of candidate profiles from a database based on the job request, each candidate profile being associated with a respective candidate; providing, as inputs to a first trained machine learning model, the job request and a first set of questions related to the job request; generating, using the first trained machine learning model, a first set of answers based on the job request and the first set of questions; providing, as inputs to the first trained machine learning model, the first plurality of candidate profiles and a second set of questions related to the candidate profiles; generating, using the first trained machine learning model, a plurality of second sets of answers based on the plurality of candidate profiles and the second set of questions (Paragraphs [33-40] Wong discloses that the system analyzes using neural network and other ML models to provide interview/assessment questions based on the candidate interview and assessments. Wong further provides the assessments based on chatbots and other collected information that fall within the interpretation of the set of questions and answers.); and
determining the one or more recommended candidates based on the first and second sets of answers; and displaying a representation of the one or more recommended candidates on a user interface (Paragraph [42]; Wong discloses that a list of recommended candidates is selected and presented based on the analysis of the interview and other candidate evaluation elements.).
Regarding claim 27, Wong discloses at least one non-transitory computer readable storage medium, storing processor-executable instructions, that when executed by a computer hardware processor, cause the processor to perform a method for matching job candidates to a job opening (Fig 6 and paragraphs [44]; Wong discloses the system elements to implement the job candidate evaluation.),
the method comprising: obtaining a job request, the job request including data related to an open job position; and determining one or more recommended candidates for the job request (Fig 1 and paragraphs [29-33]; Wong discloses providing a job description and a matching system identifying candidates in a talent pool.),
at least in part by: identifying a first plurality of candidate profiles from a database based on the job request, each candidate profile being associated with a respective candidate; providing, as inputs to a first trained machine learning model, the job request and a first set of questions related to the job request; generating, using the first trained machine learning model, a first set of answers based on the job request and the first set of questions; providing, as inputs to the first trained machine learning model, the first plurality of candidate profiles and a second set of questions related to the candidate profiles; generating, using the first trained machine learning model, a plurality of second sets of answers based on the plurality of candidate profiles and the second set of questions (Paragraphs [33-40] Wong discloses that the system analyzes using neural network and other ML models to provide interview/assessment questions based on the candidate interview and assessments. Wong further provides the assessments based on chatbots and other collected information that fall within the interpretation of the set of questions and answers.); and
determining the one or more recommended candidates based on the first and second sets of answers; and displaying a representation of the one or more recommended candidates on a user interface (Paragraph [42]; Wong discloses that a list of recommended candidates is selected and presented based on the analysis of the interview and other candidate evaluation elements.).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Wong et al [2023/0088444], hereafter Wong in view of Procter et al [2024/0354320], hereafter Procter.
Regarding claim 14, Wong discloses the method of claim 1, however, Wong does not specifically state that the ML model is a decoder-only LLM;
Procter teaches wherein the first trained machine learning model is a large language model with a decoder-only transformer architecture (Fig 6B and paragraphs [134]; Procter teaches a similar ML/AI modeling system that specifically provides a decoder LLM. Wong provides ML/AI modeling and Procter provides the specific modeling technique to analyze similar data elements.).
Wong discloses a neural network and other ML/AI-based modeling system to analyze and evaluate candidates based on nodes and other data elements, however, Wong does not specifically state that the ML model is a decoder-only LLM.
Procter teaches a similar ML/AI system that specifically provides a decoder LLM as the modeling technique.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention for the neural network and other ML/AI-based modeling system to analyze and evaluate candidates based on nodes and other data elements of Wong to include a specific decoder LLM for the ML/AI modeling technique as taught by Procter since the claimed invention is merely a combination of prior art elements and in the combination each element would have performed the same function as it did separately and one of ordinary skill in the art would have recognized the results of the combination were predictable.
Regarding claim 15, Wong discloses the above-enclosed limitation of the method of claim 14, however, Wong does not specifically teach fine-tuning;
Procter teaches wherein the first trained machine learning model is fine-tuned for analysis of candidate profiles and job requests (Fig 6B and paragraphs [134 and 150-152]; Procter teaches a similar ML/AI modeling system that specifically provides an encoder LLM with fine-tuning. Wong provides ML/AI modeling and Procter provides the specific modeling technique to analyze similar data elements.).
Wong discloses a neural network and other ML/AI-based modeling system to analyze and evaluate candidates based on nodes and other data elements, however, Wong does not specifically state that the ML model is fine-tuning a model.
Procter teaches a similar ML/AI system that specifically provides fine-tuning an LLM model.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention for the neural network and other ML/AI-based modeling system to analyze and evaluate candidates based on nodes and other data elements of Wong to include a specific fine tuning modeling technique as taught by Procter since the claimed invention is merely a combination of prior art elements and in the combination each element would have performed the same function as it did separately and one of ordinary skill in the art would have recognized the results of the combination were predictable.
Regarding claim 16, Wong discloses the above-enclosed limitations of the method of claim 2, Wong does not specifically teach encoder transformer with fine-tuning;
Procter teaches wherein the second trained machine learning model is a large language model with an encoder-only transformer architecture and is fine-tuned to analyze information related to job postings and candidate profiles (Fig 6B and paragraphs [134 and 150-152]; Procter teaches a similar ML/AI modeling system that specifically provides an encoder LLM with fine-tuning. Wong provides ML/AI modeling and Procter provides the specific modeling technique to analyze similar data elements.).
Wong discloses a neural network and other ML/AI-based modeling system to analyze and evaluate candidates based on nodes and other data elements, however, Wong does not specifically state that the ML model is an LLM encoder modeling with fine-tuning.
Procter teaches a similar ML/AI system that specifically provides an encoder LLM as the modeling technique with fine-tuning.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention for the neural network and other ML/AI-based modeling system to analyze and evaluate candidates based on nodes and other data elements of Wong to include a specific decoder LLM for the ML/AI modeling technique as taught by Procter since the claimed invention is merely a combination of prior art elements and in the combination each element would have performed the same function as it did separately and one of ordinary skill in the art would have recognized the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Stewart et al [2023/0252418] (candidate classification and evaluation based on ML analysis with respect to job description);
Hajarnis et al [2024/0330863] (HR LLM for generative job description and analysis);
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW CHASE LAKHANI whose telephone number is (571)272-5687. The examiner can normally be reached M-F 730am - 5pm (EST).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sarah Monfeldt can be reached at 571-270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ANDREW CHASE LAKHANI/Primary Examiner, Art Unit 3629
Prosecution Insights